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TREE CANOPY HEIGHT ESTIMATION USING MULTI BASELINE RVOG INVERSION
TECHNIQUE
Arun Babu 1, *, Shashi Kumar 1
1 Photogrammetry and Remote Sensing Department, Indian Institute of Remote Sensing, Uttrakhand, India –
[email protected], [email protected]
Commission V, SS: Emerging Trends in Remote Sensing
KEYWORDS: PolInSAR, Tree Canopy Height, Random Volume over Ground, UAVSAR, AfriSAR, coherence
ABSTRACT:
Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) technique utilizes the characteristics of both SAR polarimetry and
Interferometry. PolInSAR technique is proved to be very useful for vegetation parameters retrieval. Estimation of the tree canopy
height parameter is very important for the estimation of the Above Ground Biomass (AGB). The baseline separation between different
PolInSAR datasets has a very important role in the tree canopy height estimation due to the sensitivity of the baseline to the tree height
and the forest structure. So for accurately estimating the tree canopy height of a forest with varying tree heights and species several
pairs of PolInSAR datasets with different baselines separations are required. Multi-baseline Random Volume over Ground (RVoG)
inversion technique is the most successful method for tree height inversion. UAVSAR, the Quad-Pol L-band airborne SAR of
JPL/NASA acquired PolInSAR datasets over the Gabon forest as a part of the AfriSAR campaign. Nine PolInSAR SLC datasets of
this campaign acquired over the Mondah Forest site of Gabon forest is used for this study. Tree canopy height map produced from this
datasets shows that the tree height is varying at this site and has a maximum height of 50 m. The results obtained are validated using
the field data collected by JPL/NASA during March 2016. The comparison of the results with the field data showed that both are in
good agreement with an average deviation of 3.75 m.
1. INTRODUCTION
Forests play an important role in Earth’s carbon cycle by
absorbing carbon from the atmosphere and storing it in the form
of above ground biomass. Tree canopy height is a very important
parameter required for the estimation of aboveground biomass
(Mohd Zaki et al., 2018). Remote sensing techniques are capable
of estimating tree canopy height quickly in a regional as well as
global scale without the requirement of rigorous fieldwork.
Synthetic Aperture Radar (SAR) techniques are preferred for this
purpose because of its all-weather, all-time operational capability
and also because of its capability of L-band and P-band to
penetrate the forest canopy.
SAR Polarimetry, Polarimetric SAR Interferometry (PolInSAR)
and SAR tomography have shown its potential for tree canopy
height estimation. SAR polarimetry uses various scattering
models to analyse the forest structure and to retrieve the forest
parameters (Sai Bharadwaj et al., 2015). SAR tomography is a
still-emerging technique which is capable of estimating the 3D-
vertical profile of the forest structure (Kumar et al., 2017a).
PolInSAR uses a coherent combination of interferograms in
different polarizations. The interferograms help to estimate the
spatial diversity of the forest vertical structure and hence the
accurate measurement of the scattering centres. While the
different polarizations available, which are sensitive to shape,
dielectric property and orientation of the scattering elements help
to identify the scattering mechanisms in a single resolution cell.
PolInSAR technique uses various inversion models to estimate
the forest parameters. Random Volume over Ground (RVoG)
model is the most successful inversion model for the estimation
of tree canopy height. This model utilizes the volume
decorrelation for tree height retrieval(Kumar et al., 2017b). The
inversion of RVoG model using single baseline PolInSAR data
assumes that in at least one of the observed polarization channels
* Corresponding author
(usually the cross-polarized HV channel), the effective ground to
volume ratio is small. However, in some cases when vegetation
is thick, dense, or the penetration of the electromagnetic wave is
weak, the assumption fails (Zhou et al., 2009). The accurate
forest height determination using PolInSAR requires an ideal
baseline between the dataset pairs which is further dependent on
the vertical structure of the forest, tree height and platform-target
geometry. So no single baseline can accurately estimate the tree
canopy height for both short and tall forest areas. RVoG
inversion model using multi-baseline PolInSAR datasets can be
used to mitigate this problem (Lee et al., 2011). Multi-baseline
datasets for PolInSAR processing refers to the coregistered set of
datasets acquired in different tracks with zero horizontal spatial
baseline separation and with different vertical spatial baseline
separation. The length of the vertical baselines between the tracks
determines the sensitivity of the interferometric phase differences
to the radar scatterers of different height. Shorter baselines are
capable of estimating the height of taller trees while longer
baselines are optimum for estimating the height of shorter trees.
The PolInSAR technique requires interferometric pair Quad-Pol
datasets with appropriate baselines.
The Uninhabited Aerial Vehicle Synthetic Aperture Radar
(UAVSAR) is the airborne Quad-Pol SAR system developed by
JPL/NASA. It operates in L-band in a frequency range from
1217.5 MHz to 1297.5 MHz and employs an electronically
scanned phased array radar. The radar is attached in a pod
mounted to the fuselage of a Gulfstream III aircraft. UAVSAR
nominally files at an altitude of 12.5 km and maps a 22 km swath
with incidence angle varying from 25 degrees to 60 degrees. The
primary design objective of the radar is to collect repeat-pass
interferometric data. For achieving this purpose the electronic
beam steering system of the SAR antenna is tied to the inertial
navigation unit of the aircraft to maintain beam pointing accuracy
regardless of the platform yaw. The UAVSAR platform was also
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-5, 2018 ISPRS TC V Mid-term Symposium “Geospatial Technology – Pixel to People”, 20–23 November 2018, Dehradun, India
This contribution has been peer-reviewed. https://doi.org/10.5194/isprs-archives-XLII-5-605-2018 | © Authors 2018. CC BY 4.0 License.
605
modified to incorporate a precision autopilot system which
allows the aircraft to fly within a 5 m tube. This empowers the
UAVSAR to fly a progression of flight lines with fixed
interferometric baselines. The UAVSAR SLC datasets have an
azimuth spacing of 0.6 m and a range spacing of 1.6 m (Rosen et
al., 2007).
The Congo Basin’s tropical forests known as “the second lungs
of the Earth”, covering more than 198 million hectares are the
second largest in the world after those of the Amazon Basin. The
Gabon forest which is a part of this Congo Basin covers an area
between 17 to 20 million hectares which is almost 80% of the
entire country. Gabon’s forests are huge carbon reservoirs
sequestering 0.94 to 5.24 gigatons of carbon.
In 2015 NASA and ESA entered into a joint programme called
the “AfriSAR campaign” to collect airborne synthetic aperture
radar data, LiDAR data and field data. The primary objective of
the campaign was to collect accurate data for the calibration and
validation purposes of the upcoming spaceborne satellites for
studying the role of forests in the Earth’s carbon cycle. As a part
of this campaign, JPL /NASA’s UAVSAR acquired airborne
SAR data at different test sites of Gabon forest in March 2016.
Field data is also collected at selected plots in this test site during
the same month.
In this paper, the tree canopy height estimation using UAVSAR
Quad-Pol L-band PolInSAR datasets acquired as part of
AfriSAR campaign and the multi-baseline RVOG inversion
technique is discussed.
2. DATASETS AND METHODS
2.1 Datasets
The datasets used for this study is the UAVSAR Quad-Pol
PolInSAR datasets acquired over the Mondah forest area test site
of the Gabon forest (Figure 1). Mondah forest area is very
suitable for multi-baseline RVoG model studies because of the
presence of short mangroves and very tall Mahogany trees
providing large diversity in tree heights. The data is acquired on
6th March 2016 in 9 tracks producing 9 sets of Quad-Pol datasets
with nearly zero horizontal baseline separation and different
vertical baselines (Table 1).
2.2 Methods
2.2.1 Methodology for Tree canopy height estimation: The
flowchart of the methodology is shown in (Figure 2). Initially
12:5 multilooking is done to the UAVSAR SLC datasets having
an azimuth spacing of 0.6 m and a range spacing of 1.6 m to
produce the MLC datasets with an azimuth spacing of 12 m and
range spacing of 8 m.
The MLC datasets are then converted to Pauli basis scattering
vector (�⃗� ) by assuming scattering reciprocity (Cloude et al.,
2001, 1998; Denbina et al., 2018; Kumar et al., 2017b; Lee et al.,
2009)
�⃗� = 1
2 [𝑆𝐻𝐻 + 𝑆𝑉𝑉 , 𝑆𝐻𝐻 − 𝑆𝑉𝑉 , 2𝑆𝐻𝑉] (1)
Figure 1. Study Area
Track
No
Date of
Acquisition
(dd/mm/yyyy)
Vertical
Baseline (m)
1 06/03/2016 Reference track
2 06/03/2016 0
3 06/03/2016 0
4 06/03/2016 45
5 06/03/2016 45
6 06/03/2016 45
7 06/03/2016 60
8 06/03/2016 60
9 06/03/2016 60
Table 1. Metadata of datasets
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-5, 2018 ISPRS TC V Mid-term Symposium “Geospatial Technology – Pixel to People”, 20–23 November 2018, Dehradun, India
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606
Equation (1) is calculated for all the pixels in the SLC datasets of
9 tracks. The scattering vector for a track ‘m’ is represented
as 𝑘⃗⃗⃗ 𝑚. The scattering vectors of all the 9 tracks are stacked
together to form the multi-track scattering vector �⃗⃗� represented
below:
�⃗⃗� =
[ �⃗� 1
�⃗� 2……
�⃗� 9]
(2)
The covariance matrix for all the 9 tracks are computed as
follows:
𝑇 = ⟨𝐾 ⃗⃗ ⃗�⃗⃗� 𝐻⟩ (3) Where ‘H’ represents the Hermitian matrix.
The matrix T for multi- baseline PolInSAR datasets is
represented as follows (Denbina et al., 2018; Neumann et al.,
2010):
𝑇𝑀𝐵 =
[
𝑇1 Ω1,2 … Ω1,𝑀
Ω1,2𝐻 𝑇2 … Ω2,𝑀
… … … …Ω1,9
𝐻 Ω2,9𝐻 … 𝑇9 ]
(4)
The 𝑇𝑀 & Ω𝑚,𝑛 matrices are separate 3 x 3 matrices, where 𝑇𝑀 is
the polarimetric covariance matrix & & Ω𝑚,𝑛 is the polarimetric
& interferometric covariance matrix for the reference track ‘m’
& secondary track ‘n’.
The complex coherence 𝛾 for any desired baseline pair is
estimated as follows (Cloude et al., 1998; Joshi et al., 2016;
Kumar et al., 2018):
𝛾 = �⃗⃗� 𝐻 Ω𝑚,𝑛 �⃗⃗�
√(�⃗⃗� 𝐻 𝑇𝑀 �⃗⃗� )(�⃗⃗� 𝐻 𝑇𝑛 �⃗⃗� ) (5)
In equation (5), �⃗⃗� is the complex polarimetric weight factor
which weights the linear combinations of polarizations to use for
computing the coherence.
The canopy- dominated coherence (𝛾ℎ𝑖𝑔ℎ) and ground dominated
(𝛾𝑙𝑜𝑤) coherence are estimated by using a phase diversity
coherence optimization technique which maximizes the value
of |𝛾ℎ𝑖𝑔ℎ − 𝛾𝑙𝑜𝑤 | (Denbina et al., 2018; Flynn et al., 2002).
For tree height estimation using multi-baseline RVoG inversion
technique, dataset pairs with suitable baseline need to be selected
for each pixel. This is by selecting baselines having the large
separation between canopy dominated coherence and ground
dominated coherence, high overall coherence and
having strong phase diversity as a function of
polarization. This is represented mathematically as
follows (Denbina et al., 2018):
𝑝𝑟𝑜𝑑 = (|𝛾ℎ𝑖𝑔ℎ − 𝛾𝑙𝑜𝑤 |)(|𝛾ℎ𝑖𝑔ℎ + 𝛾𝑙𝑜𝑤 |) (6)
The baseline with the highest value of ‘prod' is selected
as the optimum baseline for each pixel.
The mathematical representation of RVoG inversion
technique is as follows (Cloude et al., 2003; Treuhaft et
al., 1996):
𝛾𝑟𝑣𝑜𝑔 = 𝑒𝑗𝜙𝜇 + 𝛼𝑣𝑡 𝛾𝑣
𝜇 + 1 (7)
Where 𝛾𝑟𝑣𝑜𝑔 is the modelled complex coherence, 𝜙 is
the interferometric phase of the ground, 𝜇 is the ground
to volume scattering amplitude ratio, 𝛾𝑣 is the volume
coherence and 𝛼𝑣𝑡 is the temporal decorrelation factor.
The volume coherence 𝛾𝑣 is estimated using the
following equations (Cloude et al., 2001, 2003; Kugler
et al., 2015):
𝛾𝑣 = 𝑝1 (𝑒
𝑝2ℎ𝑣 − 1)
𝑝2 (𝑒𝑝1ℎ𝑣 − 1)
(7)
𝑝1 = 2𝜎𝑥 𝑐𝑜𝑠𝜏𝑐
sin(𝜃 − 𝜏𝑐) (8)
𝑝2 = 𝑝1 + 𝑗𝑘𝑧 (9)
Where ℎ𝑣 is the forest canopy height, 𝜎𝑥 is the extinction
coefficient of microwaves within the forest canopy (Np/m), 𝑘𝑧 is
the interferometric vertical wavenumber (rd/m) and 𝜏𝑐 is the
slope angle of the underlying terrain.
The 𝑘𝑧 file provided by JPL/NASA is used for estimating the
other parameters and the 𝜏𝑐 is assumed as zero.
The interferometric ground coherence 𝑒𝑗𝜙 is estimated by using
a line fit between the optimized coherences 𝛾ℎ𝑖𝑔ℎ and 𝛾𝑙𝑜𝑤. The
ground coherence is found at one of the intersections between the
fitted line and the unit circle. Out of the two intersections with
the unit circle the value which gives the height of the observed
phase centre less than 𝜋 𝑘𝑧⁄ is taken as the ground coherence.
After estimating all the unknowns, the equation (7) is solved for
estimating the tree canopy height (ℎ𝑣).
Figure 2. Flowchart of Methodology
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-5, 2018 ISPRS TC V Mid-term Symposium “Geospatial Technology – Pixel to People”, 20–23 November 2018, Dehradun, India
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607
2.2.2 Methodology for result validation using field data:
The result obtained is validated using the field data collected by
JPL/NASA during March 2016. The field data is collected by
measuring the tree canopy heights at the 0.25 ha and 1 ha plots
selected at the Mondah study area. Sixteen 0.25 hectare plots are
used for validating the result obtained. Initially, shapefile of the
plots are produced and overlaid with the raster height map. The
pixels lying inside the plots are selected and 4 pixels in azimuth
and 6 pixels in range direction are averaged together to produce
a pixel size of 48 m x 48 m which match closely with the 50 m x
50 m plots on the ground.
JPL/NASA collected field data from Mondah forest area which
covers the swath of the UAVSAR PolInSAR datasets during
March 2016. The field data is collected by measuring the tree
canopy heights at 0.25 ha (50 m x 50 m) and 1 ha plots selected
at the study area. The heights of the trees were measured at these
plots and averaged to produce a single tree canopy height value
for that particular plot.
Sixteen 0.25 ha plots are used for validating the results obtained.
Initially, the shapefile of the plots are produced and overlaid with
the raster height map. The pixels lying inside the plots are
selected and 4 pixels in azimuth and 6 pixels in range direction
are averaged together to produce a pixel size of 48 m x 48 m
which match closely with the 50 m x 50 m plots on the ground.
The estimated tree heights are then compared with the field data
to validate the results.
3. RESULTS AND DISCUSSIONS
Multi-baseline RVoG inversion using the above-described
methodology is performed and obtained the results which are
discussed below:
The figure 3 shows the canopy dominated coherence image of the
study area. Canopy dominated coherence indicates the volume
scattering component from the tree structure. Estimating the
canopy coherence is required to identify the scattering phase
centres which gives information about the vertical depth of the
Figure 3. Canopy dominated coherence
Figure 4. Ground dominated coherence
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-5, 2018 ISPRS TC V Mid-term Symposium “Geospatial Technology – Pixel to People”, 20–23 November 2018, Dehradun, India
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608
forest. From the figure, it can be seen that the barren
lands in the study area are having very high coherence
greater than 0.8 shown in red colour. The vegetated areas
are having medium coherence in the range 0.5 to 0.75
shown in green to the yellow colour range. The reduced
value of canopy dominated coherence in the vegetated
areas is due to the volume decorrelation introduced
mainly due to the wind which alters the orientation of the
leaves. Since the datasets are acquired on the same day
other atmospheric effects are negligible. The blue
regions are the water bodies having very low coherence.
The Ground dominated coherence of the study area is
shown in figure 4. Ground dominated coherence
indicates the surface and double-bounce scattering
components from the ground beneath the forest canopy.
By comparing figure 3 and figure 4 it can be seen that
both the canopy dominated coherence and ground
dominated coherence are almost similar with an only a
small difference in between. As seen in the canopy
dominated coherence image, the barren lands are having
the highest ground coherence of 0.8 and above in figure
4 also. The vegetated areas are having medium ground
coherence from 0.5 to 0.78.
From the figure 5 it can be seen that in vegetated areas where the
thick forest is present the value of Ground dominated coherence
is slightly higher than the Canopy dominated coherences.
It is due to the capability of the L-Band EM wave to
penetrate more to the ground through the forest canopy
and also due to the absence of volume decorrelation.
Both the canopy dominated coherence and ground
dominated coherence are used for the coherence
optimization procedure to identify the baseline pairs
which offer maximum separation between these
coherences and hence the respective scattering phase
centres which are very important for accurate tree canopy
height estimation.
Figure 6 shows the coherence region plot of a single pixel. Inside
the unit circle, the coherence region itself is shown as the solid
Figure 5. Ground vs Canopy dominated coherence
Figure 6. Coherence Region plot
Figure 7. Tree Canopy height
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-5, 2018 ISPRS TC V Mid-term Symposium “Geospatial Technology – Pixel to People”, 20–23 November 2018, Dehradun, India
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609
blue line. Each of the standard lexicographic and Pauli basis
coherences is plotted as different coloured dots. The HV
coherence is shown in light green. The complex coherences with
maximum separation estimated through phase diversity
optimization technique is shown by the dark green and brown
dots located on the edge of the coherence region. The high
coherence shown in dark green has the lowest ground
contribution of any polarization in the data. The low coherence
shown in brown has the highest ground contribution. The dashed
green line is the line fitted to these optimized coherences. At the
points where this line intersects the unit circle, there are two
coherences plotted, one in black, and one in orange. The black
dot is the ground coherence chosen by the algorithm as per the
methodology described above, while the orange dot is the other
alternate ground solution which was discarded.
Even though the HV coherence is close to the optimized high
coherence, they are not equal. This is because the HV coherence
usually contains a small amount of ground backscattering
compared to most of the other polarizations, it is almost never the
polarization with the absolute smallest amount of ground
backscattering out of all possible polarization states. This is the
reason for the requirement of coherence optimization.
Figure 7 shows the tree canopy height estimated using multi-
baseline RVoG inversion technique. The white colour regions
show the water bodies and barren lands which are masked out
from the process and assigned with zero height values. The
lighter shades of green indicate lower tree canopy height values
and the darker shades of green indicates higher tree canopy
height. The tree canopy height estimated reaches up to a
maximum height of 50 m.
Figure 8 shows the graph between the vertical wave number and
tree canopy height. By analysing the graph it can be seen that the
vertical wave number values are varying randomly with respect
to different tree canopy height values and it is not possible to
establish a relationship between the changes in vertical wave
number values with respect to the tree canopy height.
The field data collected from sixteen 0.25 ha
plots from the study area is used to validate
the results obtained through RVoG inversion
technique. The pixels of the tree canopy
height results area averaged as per the
methodology described in the previous
section to match the spatial resolution of the
results with the extent of the field plots on the
ground. The plots are selected at distributed at
locations to cover different range locations.
Figure 9 shows the graph between estimated
tree height and the tree height data collected
from the field. By analysing the graph it can
be seen that the estimated tree height results
are in good agreement with the field data. The
results are having a good match in the 30 m to
40 m range and with slightly more deviation
in the 10 m to 20 m range and also at 40 m to
50 m range. This can be due to the
unavailability of appropriate baselines for these ranges. The
overall deviation between the field data and the estimated tree
canopy height is 3.75 m.
4. CONCLUSIONS
Estimation of the forest canopy height is very important for the
estimation of carbon stock present in forests. Remote sensing is
the ideal method to estimate the forest canopy height on a global
scale very accurately with limited expenses and fieldwork. The
availability of Quad-Pol PolInSAR datasets with different
baselines makes the multi-baseline RVoG inversion technique an
ideal candidate for this purpose. The results
obtained through this study shows that the tree
canopy height retrieved through this technique
is in a good match with the field data.
ACKNOWLEDGEMENTS
We express our sincere thanks to JPL/NASA
for providing the UAVSAR datasets, field
data for validation and the Kapok python
library for the Multi-baseline PolInSAR
processing. We are also grateful to Indian
Institute of Remote Sensing, Dehradun for
providing all the necessary support and
infrastructure required for carrying out this
study.
Figure 8. Vertical wave number vs tree canopy height
Figure 9. Field data vs estimated tree height
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-5, 2018 ISPRS TC V Mid-term Symposium “Geospatial Technology – Pixel to People”, 20–23 November 2018, Dehradun, India
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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-5, 2018 ISPRS TC V Mid-term Symposium “Geospatial Technology – Pixel to People”, 20–23 November 2018, Dehradun, India
This contribution has been peer-reviewed. https://doi.org/10.5194/isprs-archives-XLII-5-605-2018 | © Authors 2018. CC BY 4.0 License.
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